Search Results for author: Caswell Barry

Found 9 papers, 1 papers with code

Probing neural representations of scene perception in a hippocampally dependent task using artificial neural networks

no code implementations CVPR 2023 Markus Frey, Christian F. Doeller, Caswell Barry

Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to inferior temporal cortex (IT).

Hippocampus Semantic Segmentation +1

Functional Connectome: Approximating Brain Networks with Artificial Neural Networks

no code implementations23 Nov 2022 Sihao Liu, Augustine N Mavor-Parker, Caswell Barry

We show that trained deep neural networks are able to perform zero-shot generalisation in novel environments, and allows for a wealth of tasks such as decoding the animal's location in space with high accuracy.

Temporally Extended Successor Representations

no code implementations25 Sep 2022 Matthew J. Sargent, Peter J. Bentley, Caswell Barry, William de Cothi

We show that in environments with dynamic reward structure, t-SR is able to leverage both the flexibility of the successor representation and the abstraction afforded by temporally extended actions.

A Simple Approach for State-Action Abstraction using a Learned MDP Homomorphism

no code implementations14 Sep 2022 Augustine N. Mavor-Parker, Matthew J. Sargent, Andrea Banino, Lewis D. Griffin, Caswell Barry

Consequently, impressive improvements in sample efficiency have been achieved when a suitable MDP homomorphism can be constructed a priori -- usually by exploiting a practioner's knowledge of environment symmetries.

Escaping Stochastic Traps with Aleatoric Mapping Agents

1 code implementation8 Feb 2021 Augustine N. Mavor-Parker, Kimberly A. Young, Caswell Barry, Lewis D. Griffin

Exploration in environments with sparse rewards is difficult for artificial agents.

Generalisation of structural knowledge in the hippocampal-entorhinal system

no code implementations NeurIPS 2018 James C. R. Whittington, Timothy H. Muller, Shirley Mark, Caswell Barry, Timothy E. J. Behrens

We propose that to generalise structural knowledge, the representations of the structure of the world, i. e. how entities in the world relate to each other, need to be separated from representations of the entities themselves.

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